No abstract

The comprehensive mathematical model described here can predict the iron transport through BBB ECs considering various possible routes from blood side to brain side. The model can also predict the transferrin and iron transport behavior in iron-enriched and iron-depleted cells, which has not been addressed in previous work.

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.

Solid-state nanopore based electro-deformation coupled with deep learning to distinguish AAV particles based on their cargo content.

Searches for new physics by the CMS collaboration are interpreted in the framework of the phenomenological minimal supersymmetric standard model (pMSSM). The data samples used in this study were collected at √ s = 7 and 8 TeV and have integrated luminosities of 5.0 fb −1 and 19.5 fb −1 , respectively. A global Bayesian analysis is performed, incorporating results from a broad range of CMS supersymmetry searches, as well as constraints from other experiments. Because the pMSSM incorporates several well-motivated assumptions that reduce the 120 parameters of the MSSM to just 19 parameters defined at the electroweak scale, it is possible to assess the results of the study in a relatively straightforward way. Approximately half of the model points in a potentially accessible subspace of the pMSSM are excluded, including all pMSSM model points with a gluino mass below 500 GeV, as well as models with a squark mass less than 300 GeV. Models with chargino and neutralino masses below 200 GeV are disfavored, but no mass range of model points can be ruled out based on the analyses considered. The nonexcluded regions in the pMSSM parameter space are characterized in terms of physical processes and key observables, and implications for future searches are discussed. Keywords: Hadron-Hadron scattering (experiments), SupersymmetryArXiv ePrint: 1606.03577Open Access, Copyright CERN, for the benefit of the CMS Collaboration. Article funded by SCOAP 3 .doi : The CMS collaboration 35 IntroductionSupersymmetry (SUSY) [1-6] is a strongly motivated candidate for physics beyond the standard model (SM). Searches for the superpartner particles (sparticles) predicted by SUSY performed in a variety of channels at the CERN LHC at √ s = 7 and 8 TeV have been reported [7][8][9][10][11][12][13][14][15][16][17][18]. The results, found to be consistent with the SM, are interpreted as limits on SUSY parameters, based mostly on models with restricted degrees of freedom, such as the constrained minimal supersymmetric standard model (cMSSM) [19][20][21][22][23][24][25], or, more recently, within the simplified model spectra (SMS) approach [26][27][28]. The cMSSM models feature specific relations among the soft-breaking terms at some mediation scale that translate into specific mass patterns typical for the model. While this problem is avoided in the SMS approach, the signatures of realistic models cannot always be fully covered by SMS topologies. This holds true, for instance, in the case of long decay chains that do not correspond to any SMS, t-channel exchanges of virtual sparticles in production, or the presence of multiple production modes that overlap in kinematic distributions. In the work reported here, data taken with the CMS experiment at the LHC are revisited with an alternative approach that is designed to assess more generally the coverage of SUSY parameter space provided by these searches. The method is based on the minimal supersymmetric standard model (MSSM) and combines several search channels and external constraints. Giv...

This mathematical study will assist in designing new drug carriers to overcome the drug delivery problems in brain. Furthermore, we anticipate that this model will form the basis of future comprehensive models for drug transport across BBB.

Time-periodic electroosmotic flow (EOF) with heterogeneous surface charges on channel walls can potentially be used to mix species or reagent molecules in microfluidic devices. Although significant research efforts have been placed to understand different aspects of EOF, its role in the mixing process is still poorly understood, especially for non-homogeneous surface charge cases. In this work, dynamic aspects of EOF in a cylindrical capillary are analyzed for heterogeneous surface charges. Closed form analytical solutions for time-periodic EOF are obtained by solving the Navier–Stokes equation. An analytical expression of induced pressure is also obtained from the velocity field solution. The results show that several vortices can be formed inside the microchannel with sinusoidal surface charge distribution. These vortices change their pattern and direction as the electric field change its strength and direction with time. In addition, the structure and strength of the vorticity depend on the frequency of the external electric field and the size of the channel. As the electric field frequency or channel diameter increases, vortices are shifted towards the channel surface and the perturbed flow region becomes smaller, which is not desired for effective mixing. Moreover, the number of vorticities depends on the periodicity of the surface charge.

Mixing in a microfluidic device is a major challenge due to creeping flow, which is a significant roadblock for development of lab-on-a-chip device. In this study, an analytical model is presented to study the fluid flow behavior in a microfluidic mixer using time-periodic electro-osmotic flow. To facilitate mixing through microvortices, nonuniform surface charge condition is considered. A generalized analytical solution is obtained for the time-periodic electro-osmotic flow using a stream function technique. The electro-osmotic body force term is accounted as a slip boundary condition on the channel wall, which is a function of time and space. To demonstrate the applicability of the analytical model, two different surface conditions are considered: sinusoidal and step change in zeta potential along the channel surface. Depending on the zeta potential distribution, we obtained diverse flow patterns and vortices. The flow circulation and its structures depend on channel size, charge distribution, and the applied electric field frequency. Our results indicate that the sinusoidal zeta potential distribution provides elliptical shaped vortices, whereas the step change zeta potential provides rectangular shaped vortices. This analytical model is expected to aid in the effective micromixer design.